GSA Connects 2021 in Portland, Oregon

Paper No. 220-16
Presentation Time: 9:00 AM-1:00 PM


DEAL, Anne1, CALLAHAN, Owen A.2, UKAR, Esti3, ZAHM, Chris4, ALLEN, Spencer5, DUNAWAY, Kori5, EPPLER, Michael5, LOERA, Alica5 and POLACH, Dustin5, (1)Texas State University, San Marcos, TX 78666, (2)Bureau of Economic Geology, The University of Texas at Austin, Austin, TX 78712, (3)Bureau of Economic Geolgoy, Austin, TX 73301, (4)Bureau of Economic Geology, The University of Texas at Austin, 10100 Burnet Road, Austin, TX 78758, (5)Texas State University, san marcos, TX 78666

Fracture network characterization varies depending on the scale at which observations are made and/or based on the observer’s expertise. We examine how these differences manifest in a dataset derived from the same scanline measured using UAV-acquired images flown at different heights and compare with data measured in the field by groups with varying expertise. We recorded fracture spacings along a 127m, ~NE-SW oriented scanline in an exposure in the footwall of the Pennsylvanian Marble Falls Fault. UAV-acquired, orthorectified images were flown at 100, 30, and 10m above the outcrop. Fracture spacings and apertures of the NW-striking fracture set were measured along the first 36m of the scanline on the ground by experienced geologists; subsets of this section were characterized by groups of undergraduate students. Fracture spatial arrangements of the datasets were evaluated using CorrCount software (Marrett et al., 2018).

At 100m we identified 97 NW-striking fractures along the 127m-long scanline, whereas 107 and 164 were in the 30m and 10m images, respectively. A correlation count analysis of the 100m dataset indicates the fracture spatial arrangement mostly indistinguishable from random, but the 30m and 10m datasets show a weak log-periodic arrangement of clusters, with cluster widths between ~2.1 and 2.7m. When restricted to the first 36m of the scanline, data is scarce and the spatial arrangement is indistinguishable from random, except for a weak signal of 2.5m-wide clusters in the 10m dataset. Only 39 fractures were identified in the first 36m of the scanline in the 10m imagery; even fewer fractures in the lower-resolution images. The same section of outcrop contained 118 fractures identified by experts on the ground.

The 36m scanline was divided into 3 sections and each section was measured by 2 different groups of undergraduate students. The regularly spaced cluster pattern is maintained in the first two subsections and was identified by all groups. Experts consistently recorded more fractures per unit length than the novice groups. Major spatial patterns were similar among all groups that measured enough fractures to have a statistically robust sample size. Even the highest resolution UAV images missed ~2/3 of the fractures visible in outcrop, producing datasets with too few fractures for significant analysis in most cases. This suggests that fracture network characterization is more consistent and repeatable between persons with different expertise than it is between datasets with different resolution.